Abstract

Abstract. Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately and systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution and a long time span. In this study, the first 1 km resolution global cropland proportion dataset for 10 000 BCE–2100 CE was produced. With the cropland map initialized in 2010 CE, we first harmonized the cropland demands extracted from the History Database of the Global Environment 3.2 (HYDE 3.2) and the Land-Use Harmonization 2 (LUH2) datasets and then spatially allocated the demands based on the combination of cropland suitability, kernel density, and other constraints. According to our maps, cropland originated from several independent centers and gradually spread to other regions, influenced by some important historical events. The spatial patterns of future cropland change differ in various scenarios due to the different socioeconomic pathways and mitigation levels. The global cropland area generally shows an increasing trend over the past years, from 0×106 km2 in 10 000 BCE to 2.8×106 km2 in 1500 CE, 6.2×106 km2 in 1850 CE, and 16.4×106 km2 in 2010 CE. It then follows diverse trajectories under future scenarios, with the growth rate ranging from 16.4 % to 82.4 % between 2010 CE and 2100 CE. There are large area disparities among different geographical regions. The mapping result coincides well with widely used datasets at present in both distribution pattern and total amount. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The spatiotemporally continuous and conceptually consistent global cropland dataset serves as a more comprehensive alternative for long-term earth system simulations and other precise analyses. The flexible and efficient harmonization and downscaling framework can be applied to specific regions or extended to other land use and cover types through the adjustable parameters and open model structure. The 1 km global cropland maps are available at https://doi.org/10.5281/zenodo.5105689 (Cao et al., 2021a).

Highlights

  • Land use changes driven by humans have profound impacts on climate change, biogeochemical cycling, biodiversity, energy supply, and food security (Foley et al, 2005; Kalnay and Cai, 2003; Ito and Hajima, 2020; Poschlod et al, 2005)

  • We produced a global cropland percentage map at 1 km resolution from 10 000 BCE to 2100 CE based on our proposed harmonization and downscaling framework, and we identified the cropland distribution patterns, estimated the cropland areas, and compared the mapping results with other datasets

  • The future demands during 2010 CE– 2100 CE came from the total areas of all five crop types in the Land-Use Harmonization 2 (LUH2) dataset, which was consistent with the design of CMIP6 and is widely used in Earth system models (ESMs) simulations (Hurtt et al, 2020)

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Summary

Introduction

Land use changes driven by humans have profound impacts on climate change, biogeochemical cycling, biodiversity, energy supply, and food security (Foley et al, 2005; Kalnay and Cai, 2003; Ito and Hajima, 2020; Poschlod et al, 2005). Significant progress has been made in agricultural monitoring, including cropland extents (Yu et al, 2013; Lu et al, 2020), cropland types (Cao et al, 2021b), crops (Zhong et al, 2014; Bargiel, 2017), and farming practices (Biradar and Xiao, 2011; Estel et al, 2015), providing basic and direct information to support specific research and management for specific years or periods. In comparison, simulating or analyzing the effect of cropland change from the beginning of farming to the end of this century can provide a comprehensive view for understanding agriculture, which is of great significance for establishing long-term environmental or economic strategies (Olofsson and Hickler, 2008; Pongratz et al, 2009; Molotoks et al, 2018; Zabel et al, 2019). Accurate global cropland change information, especially a harmonized cropland dataset at high resolution from past to future, plays a crucial role in improving the simulation accuracy and supporting the detailed analysis

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